Predicting novel substrates for enzymes with minimal experimental effort with active learning.

Autor: Pertusi DA; Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, United States., Moura ME; Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, United States., Jeffryes JG; Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, United States; Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL, United States., Prabhu S; Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, United States., Walters Biggs B; Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, United States., Tyo KEJ; Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, United States. Electronic address: k-tyo@northwestern.edu.

Publikováno v: Metabolic engineering [Metab Eng] 2017 Nov; Vol. 44, pp. 171-181. Date of Electronic Publication: 2017 Oct 10.

EP-Pred: A Machine Learning Tool for Bioprospecting Promiscuous Ester Hydrolases.

Autor: Xiang, Ruite¹ (AUTHOR), Fernandez-Lopez, Laura² (AUTHOR), Robles-Martín, Ana¹ (AUTHOR), Ferrer, Manuel² (AUTHOR), Guallar, Victor^1,3 (AUTHOR) victor.guallar@bsc.es

Publikováno v: Biomolecules (2218-273X). Oct2022, Vol. 12 Issue 10, pN.PAG-N.PAG. 14p.

Data-driven enzyme engineering to identify function-enhancing enzymes.

Autor: Jiang, Yaoyukun, Ran, Xinchun, Yang, Zhongyue J

Publikováno v: PEDS: Protein Engineering, Design & Selection; 2023, Vol. 36, p1-13, 13p